Artificial General Intelligence System and Method for Medicine

ABSTRACT

A medical general intelligence computer system and computer-implemented methods analyze morpho-physiological numbers for determining a risk of an emergent disease state, determining an emergent disease state, predicting a pre-emergent disease state, determining a pre-emergent disease state, and/or predicting a risk of a pre-emergent disease state.

FIELD

This disclosure generally relates to an artificial intelligence systemapplied to the medical arts and a computerized method for making amedical diagnosis.

BACKGROUND

Biological systems are complex. Complex means that the data is nonlinearand/or dynamic. Generally, determining a risk of an emergence of adisease state (e.g., emergent disease state; a phenotype of a diseasestate being expressed) is an unsolvable problem in polynomial time,exponential time, or finite time. Further, generally, predicting apre-emergence of a disease state (e.g., pre-emergent disease state;prior to actual emergence of that disease state) is an unsolvableproblem in polynomial time, exponential time, or finite time.

SUMMARY

It will be appreciated that a “knowledgebase” and “knowledge base” aredistinctly different. A knowledgebase generally describes a databasewhich can be searched via a search engine. The search engine for data inthe database can obtain data without mandating (e.g., affirming)characteristics or relationships. In contrast, the knowledge baseincludes information within a context of an intelligent algorithm. Theintelligent algorithm uses data with validated qualities andrelationships. Thus, while the knowledgebase is merely a repository ofdata, which can be searched for the presence of specific data, theknowledge base is a domain library of specific knowledge (e.g., datawith validated relationships) and/or data with imbedded relationshipinformation.

An embodiment of a computer-implemented method for determining a diseasestate comprises a processor accessing from a computer-readablenon-transitory memory, computer-readable data (e.g., knowledge base)representing a general graph (e.g., based on user-developed or a medicalcommunity's validated knowledge base) having nodes of physiologicalconditions (e.g., state, data, and/or features). Each of thephysiological conditions is represented by each of the nodes, whereineach of the nodes is connected by one or more edges representingcorrelations between the nodes, the processor accessing from thecomputer-readable non-transitory memory, computer-readable data ofphysiological conditions of a patient, the processor executingcomputer-readable instructions for quantification of the physiologicalconditions of the patient, the processor transforming the physiologicalconditions of the patient to node data by performing quantification ofthe physiological conditions of the patient, the processor executingcomputer-readable instructions for associating the node data to thegeneral graph, the processor creating an individualized graph byassociating the node data to the general graph, the processor storingthe individualized graph to the computer-readable non-transitory memory,the processor accessing from the computer-readable non-transitorymemory, computer-readable data representing a topological module ofnodes connected by edges, wherein the topological module represents thedisease state (e.g., a computable surrogate of the disease state), andthe processor executing computer-readable instructions for mapping thetopological module to the individualized graph.

An embodiment of the computer-implemented method further comprises theprocessor mapping the topological module to the individualized graph,wherein the disease state is an emergent disease state.

An embodiment of the computer-implemented method further comprises theprocessor mapping the topological module to the individualized graph,wherein the disease state is a pre-emergent disease state.

An embodiment of a computer-implemented method for creating a graph fordetermining a disease state comprises storing on a computer-readablenon-transitory memory, computer-readable data representing a generalgraph of physiological conditions, wherein each of the physiologicalconditions is represented as a node of the general graph, wherein eachnode is connected by one or more edges representing correlations betweenthe nodes, and storing on the computer-readable non-transitory memory,computer-readable data representing a topological module of nodesconnected by edges, wherein the topological module represents thedisease state (e.g., a computable surrogate of the disease state), andthe topological module can be mapped onto a portion of the generalgraph.

An embodiment of a computer-implemented method for determining apre-emergent disease state comprises a processor accessing from acomputer-readable non-transitory memory, computer-readable datarepresenting physiological conditions represented as nodes, wherein atleast one of the nodes has a correlation to another one of the nodes,the processor accessing from the computer-readable non-transitorymemory, computer-readable data of first physiological conditions of apatient collected at a first time period, the processor transforming thefirst physiological conditions of the patient to first node data byperforming quantification of the first physiological conditions of thepatient, the processor accessing from the computer-readablenon-transitory memory, computer-readable data of second physiologicalconditions of the patient collected at a second time period, theprocessor transforming the second physiological conditions of thepatient to second node data by performing quantification of the secondphysiological conditions of the patient, the processor accessing fromthe computer-readable non-transitory memory, computer-readable datarepresenting a topological module of nodes, wherein the topologicalmodule represents the pre-emergent disease state, the processor mappingthe topological module to the first node data, the processor mapping thetopological module to the second node data, and the processordetermining the pre-emergent disease state based on the mapping thetopological module to the first node data and to the second node data.

An embodiment of a medical general intelligence computer comprises aprocessor, and a computer-readable non-transitory memory incommunication with the processor, the computer-readable non-transitorymemory including and/or stored therein one or more processor-executableinstructions for the processor to perform one or more of thecomputer-implemented methods disclosed herein.

BRIEF DESCRIPTION OF THE DRAWINGS

Referring now to the drawings in which like reference numbers representcorresponding parts throughout.

FIG. 1 illustrates a schematic box diagram of an embodiment of theMedical Reasoning System (MRS).

FIG. 2 illustrates an example of a graph, which is a visual (e.g.,schematic) representation of data stored in a non-transitory memory of aMRS.

FIG. 3 illustrates an example of a complex relationship view of a graph(of a power function distribution) between physiological features (e.g.,nodes) and their connections (e.g., edges).

FIG. 4 illustrates an example of a visual (e.g., schematic)representation of an analysis process carried out by the MRS.

FIG. 5 illustrates examples of topological modules.

FIGS. 6-8 illustrate an example of an embodiment of the ArtificialGeneral Intelligence (AGI) method.

FIG. 9 illustrates an example of the numerical analysis of node values,which can be performed by the MRS.

FIG. 10 illustrates an example of the steps carried out by the MRS usingthe AGI method.

FIG. 11 illustrates an example of an emergent disease state vs.non-emergent disease state.

FIG. 12 illustrates an example of an individual's physiological datacollected along a spatial and/or temporal dimension.

FIG. 13 illustrates an example of a chart showing a change in thedisease intensity along a time dimension.

FIG. 14 illustrates an example of a chart resulting from the MRSperforming a metadata analysis of temporal data.

DETAILED DESCRIPTION

The embodiments disclosed are directed towards a computerized generalintelligence systems applicable to the medical arts and a computerizedmethod for determining risks associated with a medical diagnosis.

Generally, without a reproducible numerical descriptor, a particularcomplex disease state is difficult to define, predict, and/or manage.Generally, useable medical data (e.g., knowledge) for defining,predicting, and/or managing diseases are numerical values associatedwith morpho-physiologic features. Such numerical values are definedherein as morpho-physiological numbers. Examples of morpho-physiologicalfeatures are, for example but not limited to, weight, heart rate, bloodpressure, velocity, hemoglobin A1c, etc. Examples ofmorpho-physiological numbers are, for example but not limited to, 80 kg(weight), 50 beats per minute (heart rate), 160 mmHg (systole)/90 mmHg(diastole) (blood pressure), 5 meters/sec (velocity), 7.0% (HemoglobinA1c) etc.

The morpho-physiological numbers are different from data rising out of aperson's genetic makeup. That is, a person's genetic makeup (DNA) isgenerally a fixed micro-level blueprint which represents a geneticpotential of the person. The person's genetic makeup does not representthe person's current physiological state at a macro-level and real worldconditions. That is, it is impossible to determine whether the person iscurrently suffering from a disease state by looking only at a specificgene of that person. Multiple morpho-physiological features and numbersof the person must be determined and analyzed to determine whether theperson is currently suffering from a particular disease and the state ofthat disease (e.g., clinical descriptor). The term physiologicalcondition(s) is used herein to describe one or more physiological state,data, and/or feature(s).

General approaches to disease diagnosis have been based on observationalcorrelations between pathological analysis and clinical data knowledgeof the syndromes. General diagnostic tools show shortcomings of thismethodology, reflecting both a lack of sensitivity identifyingpreclinical disease and lack of specificity in definingcause-and-effect. In addition, generally, diseases are principallystudied by a trial-and-error process to figure out which treatment worksfor a given patient or pooled population.

Clinical descriptors such as “congestive heart failure”, “cancer”,“hypertension”, etc. provide a name, or an identity, to a disease state.For example, a person with a blood pressure numbers(morpho-physiological numbers) of 160 mmHg (systole)/90 mmHg (diastole)can be determined to have clinical Arterial Hypertension, i.e., highblood pressure (clinical descriptor). However, a disease state of abiological system (e.g., animal, human, etc.) is rarely defined by asingle physiological feature such as blood pressure. Instead, thedisease state reflects perturbations of a complex physiological networkof linked morpho-physiological features (e.g., tissues, organs, etc.).Thus, while the clinical descriptors are a convenient way ofcommunicating in few words the complex perturbations of thephysiological network, the clinical descriptors, by themselves, have notbeen able to provide sufficient information for numerical and/orcomputational analysis needed for determining cause-and-effect risklevels and/or management levels of perturbations of complex nonlinearmorpho-physiological features. That is, the clinical descriptors havebeen considered useless for numerical evaluations because the individualclinical descriptors are not reproducible numbers defining a dynamiccomplex biological state. For example, the clinical descriptor of“cancer” (of a patient who is suffering from cancer) has not itself beendirectly used for numerical and/or computational evaluation of thepatient's current state of suffering from the disease, nor fordetermining risks of other related physiological conditions and/ordisease states that the patient may suffer from in the near or distantfuture. That is to say, clinical descriptors (e.g., names of diseases orindividual data features) were conceived for the sake of easilycommunicating complex physiological phenomenon between humans (e.g.,knowledge based communication). Clinical descriptors have not beendirectly computable. Accordingly, clinical descriptors had not been usedin determining and/or predicting individual risk levels of theseperturbations of the morpho-physiological features. Additionally,clinical descriptors had not been used in determining and/or predictingan individual's emergent disease state and/or a pre-emergent diseasestate.

Disclosed herein are complex knowledge base systems and methods thatdefine clinical descriptors as groupings of highly connectedmorpho-physiological features, such that the quantitative clinicaldescriptors can be used in determining and/or predicting risk levels(e.g., disease intensity) of these perturbations of themorpho-physiological features.

Disclosed herein are systems and methods that define clinicaldescriptors as a connection of morpho-physiological features, such thatthe clinical descriptors can be used in determining and/or predicting anemergent disease state and/or a pre-emergent disease state.

The systems and methods disclosed herein define clinical descriptors asquantifiable information. The systems and methods disclosed herein candescribe clinical descriptors as multi-dimensional computer-readabledata representing a topology of quantifiable morpho-physiologicalfeatures.

The topology is described herein as a topological module. Themorpho-physiological features are described herein as a network of nodesand/or hubs. The morpho-physiological numbers are described herein asnumerical values for the associated morpho-physiological features (nodesand/or hubs). Accordingly, the topological module is a networkrepresentation (e.g., surrogate disease module) of connected nodesand/or hubs. Thus, clinical descriptors of diseases are represented ascomputer-readable data represented as a network connections and/orrelationships of numbers associated with nodes and hubs. Accordingly,clinical descriptors, and a disease state, can also be visuallyrepresented as a graph having a network of connected nodes, wherein atleast a portion of the graph can have a topological module of nodesconnected by edges, wherein the topological module represents thedisease state. Therefore, according to the embodiments herein, clinicaldescriptors can be numerically analyzed and computed. The graph is aconnection of physiological conditions (e.g., data and/or features)represented as nodes, wherein each connection between the nodesrepresents a relationship between the two physiological conditions(e.g., causation, relation, association, correlation, etc.) based onuser-developed or a medical community's validated knowledge base. Theterm “user-developer” describes a person who identifies the data anduses the knowledge base to transform the data relationship tointelligible information which can be processed by a processor.

A Medical Reasoning System (MRS) can be a specialized medical generalintelligence computer configured to perform the computer-implementedmethods disclosed herein. Embodiments of the MRS can have a processorand a computer-readable non-transitory memory in communication with theprocessor. The computer-readable non-transitory memory can store thereinone or more processor-executable instructions for the processor toperform one or more of the computer-implemented methods disclosedherein.

FIG. 1 illustrates a box diagram of an embodiment of the MRS 100. TheMRS 100 can be a specialized medical general intelligence computerconfigured to perform one or more of the computer-implemented methodsdisclosed above. The MRS 100 comprises one or more processor(s) 102, anda computer-readable non-transitory memory 104 in communication with theone or more processor(s) 102. One or more of the computer-readablenon-transitory memory 104 stores therein one or moreprocessor-executable instructions for the one or more processor(s) 102to perform one or more of the computer-implemented methods disclosedabove. The computer-readable non-transitory memory 104 can store one ormore of computer-readable data, graph data, node data, node value, hubdata, hub value, edge data, topological module data, individualizedgraph data, meta-data, and inference data. Examples of thecomputer-readable non-transitory memory 104 are non-transitory storagemedium, such as but are not limited to, magnetic media (disc, film,ribbon, etc.), optical media (disc, film, ribbon, etc.), ROM, RAM, flashmedia, solid state drive, etc. The MRS 100 is configured with one ormore Input/Output (I/O) device(s) 106 which is in communication with oneor more of the processor(s) 102.

Examples of the I/O device(s) 106 include but are not limited to akeyboard, a mouse, a touchscreen, a display device, etc. The I/Odevice(s) 106, such as the touchscreen and/or the display device, candisplay thereon a graphical user interface (GUI) for a user to interactwith the MRS 100. The GUI can provide for displaying of information tothe user and can receive user input via the user's interaction with theGUI.

The I/O device(s) 106 are device(s) configured to allow a user tointeract with the MRS 100 and/or for the MRS 100 to interact with theuser. Accordingly, one or more I/O device(s) 106 can provide forentering data (input), displaying data (output), displaying results ofthe analysis (output), etc.

An embodiment of the MRS 100 is configured to operate acomputerized-method including an Artificial General Intelligence (AGI)method. The MRS 100 can analyze a topological module andmorpho-physiological numbers for determining and/or predicting risklevels of perturbations of the morpho-physiological features. Further,the MRS 100 can analyze a topological module and morpho-physiologicalnumbers for determining a risk of an emergent disease state. Further,the MRS 100 can analyze a topological module and morpho-physiologicalnumbers for determining an emergent disease state. The MRS 100 cananalyze a topological module and morpho-physiological numbers forpredicting a pre-emergent disease state. Further, the MRS 100 cananalyze a topological module and morpho-physiological numbers fordetermining a pre-emergent disease state. Further, the MRS 100 cananalyze a topological module and morpho-physiological numbers forpredicting a risk of a pre-emergent disease state.

An embodiment of the methods performed by the MRS 100 includes a medicalapplication of the AGI method. The AGI methods described herein aredirected to simplifying complex events. The AGI method can be applied tomany different domains. Thus, the AGI method is domain-independent. Thatis, the AGI method includes domain-independent processing hardwareand/or software, which can be applied universally and generally to allmedical diagnostic and/or assistant methodologies. Further, the AGImethod can be applied to obtaining and storing domain-specific knowledge(e.g., information, data, meta-data, etc.) using the generaldomain-independent methodology. The AGI methodology for obtaining andstoring information does need to use a data search (e.g., iterativesearch algorithm). The AGI method does not attempt to replicate and/orcompute every feature of a complex event. In contrast, the AGI methodtakes the features of the complex event, and simplifies the complexevent to few features that are determined to be essential and/orimportant to that complex event. Accordingly, the AGI method allows forextremely fast computation and/or analysis of complex events.

The computer-implemented method including the AGI can integrate“autonomous learning,” which is described herein to mean that thecomputer system can perform analysis of data without supervision by aperson. The computer-implemented method including the AGI can integrate“goal-directed learning,” which is described herein to mean that thecomputer system can perform selective analysis of data towards a goalset by the computer, without supervision by a person. Thecomputer-implemented method including the AGI can integrate an “adaptivelearning,” which is described herein to mean that the computer systemcan perform cumulative and contextual analysis of data withoutsupervision by a person. Adaptive learning can lead to additional data(e.g., new data).

The computer-implemented method including the AGI can include patternrecognition and pattern matching (e.g., mapping). Accordingly, thecomputer-implemented method including the AGI can include determining aninference between one set of data (wherein “set” can be one or morevalue(s)) to another set of data. An example of the inference betweensets of data includes sets of data for same parameters taken atdifferent times and disease types. The AGI method understand the conceptof the time-dimension for these sets of data, and based on the changesin the sets of data, an inference data can be created as meta-data forthe collective sets of the data.

The MRS 100 configured to perform the medical application of the AGImethod can perform an analysis of data without the need to usedisease-dependent language. That is, the MRS 100 can use data havingdisease-independent categorical language. The data used by the MRS 100are formulated and associated in the terms and statements derived fromexperience-grounded semantics. That is, the formulations and theassociations of the terms are pre-determined and determined based onexperiences and/or behavior arising out of research (which a user inputsinto and stored in the system memory) and/or self-learning (e.g.,autonomous, goal-directed, and/or adaptive). An example of self-learningdata includes but is not limited to inference rules of nodes determinedby the system based on the data stored in the memory.

The embodiments disclosed herein are also directed to the MRS 100 andmethods for performing an analysis (e.g., mapping) of one or moretopological modules stored in the memory in comparison to themorpho-physiological numbers of nodes and/or hubs, determining apre-emergent disease state (and/or risk(s) thereof). The embodiments canalso provide an output for suggested management of particularmorpho-physiological feature(s) for preventing and/or delaying anemergence of the pre-emergent disease state.

FIG. 2 illustrates an example of a graph 200 which is a visual (e.g.,schematic) representation of data stored in a non-transitory memory (104illustrated in FIG. 1) and processed by one or more processor(s) (102illustrated in FIG. 1) of a MRS (100 illustrated in FIG. 1). The graph200 is formed of a plurality of nodes (e.g., nodes 202, 204, 206)connected via edges (e.g., edges 208, 210, 212, 214, 216, 218, 220).Each edge 208, 210, 212, 214, 216, 218, 200 connects two nodes. Thegraph 200 includes nodes (e.g., node 202, node 204, and node 206) havingdifferent sizes to visually convey different relative magnitudes ofconnectivity for each of the nodes. For example, node 206, which hasmore connections than node 204, is represented in the graph 200 to havea larger size than the size of the node 204. The relative size varianceof the nodes based on the magnitude of connectivity can enhance aneasier understanding of the graph 200. For example, edge 208 connectsnodes 202 and 204. A node can have one edge connected thereto (e.g.,node 202 is connected to only one edge 208). A node can have multipleedges connected thereto (e.g., node 206 is connected to edges 210, 212,214, 216, 218, 220). A node (e.g., 206) having more than a set number(e.g., a predetermined number) of edges connected thereto can bedetermined (e.g., identified, designated, etc.) to be a “hub.” Forexample, if the set number is 4, then the node 206 can be determined tobe a hub. For example, if the set number is 4, then the nodes 202, 204can be determined to be “not hubs” (that is, the nodes 202 and 204cannot be determined to be hubs). Each node 202, 204 and each hub 206 ofthe graph 200 can have a numerical value associated with it. Thenumerical value can be a value representative of a quantified (e.g.,weighed measure) physiological condition of an individual. The graph 200may be a general graph, which does not have numerical values (or thenumerical values are null values) associated with each of the nodes 202,204 and each of the hubs 206.

The MRS (100 illustrated in FIG. 1) can include a processor that canaccess a non-transitory memory for building information represented bythe graph 200, modifying the information represented by the graph 200 byadding data that represent additional nodes, hubs, and/or edges,removing nodes, hubs, and/or edges, etc. Such modifications can be doneby a user interacting with the MRS and/or self-learning by the MRS byappropriate analysis of other data. For example, if a new research showsthat an association between node 202 and node 204 does not exist, thenthe processor of the MRS can access the non-transitory memory and removethe edge 208 associating the nodes 202, 204. This can be performed bythe user purposefully interfacing with the MRS to remove the datarepresenting the edge 208 of the graph 200, or the user can enter thechange in the relational information between the two features that arerepresented as nodes 202, 204 and edge 208 (e.g., by interacting withthe I/O device(s) 106 illustrated in FIG. 1), and the processor of theMRS automatically, and dynamically, changes the data representing thegraph 200 accordingly (by removing the edge 208). Addition(s) of one ormore of the nodes, hubs, and edges can also be performed by the userpurposefully interfacing with the MRS (e.g., by interacting with the I/Odevice(s) 106 illustrated in FIG. 1), or the processor of the MRSautomatically, and dynamically, changing the data representing the graph200 according to new information the MRS has access to.

Each of the nodes 202, 204, 206 represents a computer-readable data(e.g., state) of a physiological feature (e.g., biological condition ofa person, animal, etc.). Each of the edges 208, 210, 212, 214, 216, 218,220 represents a relationship (e.g., association, causation, inference,etc.) between two physiological features (e.g., the nodes). For example,in the medical domain of cardiology, early diastolic velocity of themitral valve annulus (e′) and a ratio E/e′ (wherein E is an earlytransmitral flow velocity) are related features. Thus, the noderepresenting e′ and the node representing E/e′ would have an edgeconnecting them together. Also, the node representing E and the noderepresenting E/e′ would have an edge connecting them together. Further,because E/e′ feature is related to left ventricular diastolic pressure(LVDP), a node representing E/e′ would have an edge connecting to a noderepresenting LVDP.

One or more processor(s) (102 illustrated in FIG. 1) can create a set ofdata which is representative of an individualized graph for a particularindividual by populating the nodes of the graph 200 with numericalvalues representing the quantified (e.g., weighed measure) physiologicalcondition of the individual. The numerical values may be a scaled value(e.g., 1 to 4), wherein the scaled value is a translation of realmeasured value. The processor(s) can store the individualized graph tothe memory of the MRS. For example, when the processor(s) receives anindividual's physiological data for e′ and E, the processor(s) canpopulate the respective nodes for e′, E, and E/e′ with the physiologicaldata and/or scaled values representing the received physiological databased on a set ranges (stored in the memory) for the respectivephysiological features. Accordingly, the processor(s) can create theindividualized graph for the particular individual by populating thenodes e′, E, and E/e′ with quantifiable numerical values. Theprocessor(s) can determine not to populate one or more of nodes of thegraph 200 with one or more of the received physiological data whencreating the individualized graph. Such determination can be made by theprocessor(s) when the physiological data includes information for a nodethat represents a less important feature (e.g., has only one edgeconnected to another node (which is not a hub)).

FIG. 3 illustrates an example of a complex relationship 300 view of agraph (e.g., graph 200 illustrated in FIG. 2) between physiologicalfeatures (e.g., nodes) and their connections (e.g., edges). The curve302 represents a power law relationship (e.g., distribution) between thephysiological features 304 and their interconnectivities (e.g.,connectivity) 306. The Y-axis 308 represents the number of quantifiablephysiological features 304. The X-axis 310 represents the number ofconnectivity 306 that exist between the features. According to the powerlaw curve 302 (e.g., power law distribution), there are very fewquantifiable physiological features 304, which have high connectivity306. Dimensionally compressed view 312 of the curve 302 shows that thereare many features (nodes) 314 with few connections, and that there are asmall number of features (hubs) 316 with high number of connections. Thepower law distribution is scale free (e.g., random removal of a largenumber of the less connected nodes will not disturb the network whileremoval of highly connected hubs will markedly disturb the networksystem). Taking out the highly connected hubs will break the networksystem.

FIG. 4 illustrates a visual (e.g., schematic) representation 400 of ananalysis process (e.g., mapping) carried out by one or more processor(s)(102 illustrated in FIG. 1) of the MRS (100 illustrated in FIG. 1),wherein a clinical descriptor has been transformed into a topologicalmodule 402.

The MRS can represent one or more clinical descriptors (diseases) asquantifiable association of physiological features and quantifiabledata. That is, the MRS can transform and store clinical descriptors ofdiseases as computer-readable data, wherein the computer-readable datarepresent a network of connections and/or relationships of numbers(e.g., states) associated with nodes and hubs. Accordingly, asillustrated in FIG. 4, clinical descriptors and a disease state can bevisually represented as a topological module 402 having a network ofconnected nodes.

The topological module 402 has been mapped onto a graph 404 in thevisual (e.g., schematic) representation 400. Although the visualrepresentation 400 can be displayed on a computer and/or MRS, it will beunderstood that the visual representation 400 can be stored andprocessed by the MRS and does not necessarily exemplify what must bedisplayed and/or shown on a display device (of the computer and/or MRS).The mapping illustrated in FIG. 4 can be performed by patternrecognition and/or pattern matching method performed by the processor ofthe MRS. For the visual (e.g., schematic) representation 400, thetopological module 402 is indicated by a border (e.g., surface createdby a network of nodes and edges representing the topological module402). The topological module 402 represents a group of nodes whereintheir perturbations are validated to be linked to a particular disease.The topological module 402 has nodes (features) that are considered tobe necessary to specific disease states. The MRS employing the AGImethod can segregate non-essential nodes and removes them fromconsideration (computational analysis does not include non-essentialnodes). For example, the topological module 402 includes a network ofnodes that are less connected 406 (shown as open circles). Thetopological module 402 includes nodes that are highly connected 408(shown as filled-in circles). The MRS employing the AGI method cansegregate the less connected nodes 406 (shown as open circles) asnon-essential nodes of the topological module 402 and need not considerthem for further analysis of making a medical determination and/orprediction associated with the topological module 402 and anindividual's physiological data. Only the highly connected nodes 408 (A,B, C, D) of the topological module 402 can be considered. The termhighly connected means that the number of edges connected to a node isgreater than a set number. In this example, the set number is four. Eachof the highly connected nodes 408 (A, B, C, D) have number of edgesconnected thereto that is greater than four. The set number can bechanged as needed and/or desired by the MRS and/or a user.

The processor of the MRS can create a disease module 410 into memory,represented by the highly connected nodes 408 (A, B, C, D). However, theMRS need not create a separate module 410 from the topological module402. Further analysis can be performed using the topological module 402,but considering only the highly connected nodes 408 (ignoring the lessconnected nodes 406), instead of creating a separate disease module 410into memory. Accordingly, the terms topological module and diseasemodule can be used interchangeably, wherein the term disease moduledefines a subset of domain specific highly connected nodes of thetopological module of a disease state. Accordingly, the disease module410 can be viewed as a breakdown or simplification of the topologicalmodule 402.

Further, the highly connected nodes 408 can be defined to be a macronode. The macro node is used herein to mean a network of the fewestnumber of the most highly connected disease associated nodes.Accordingly, the macro node can represent a single quantifiable measureof a feature-to-disease expression. For each clinical descriptor, amacro node can be determined by the above process. The macro nodes canbe stored into the memory of the MRS. The macro nodes can be used in aquantifiable inference process to compute the dynamical integrity(coherence, intensity, variance, etc.) of a particular emergent disease.

FIG. 5 illustrates examples of topological modules 502, 504 which can bemapped onto a graph 506. Each of the topological modules 502, 504represents a disease state (e.g., clinical descriptor). The MRS (100illustrated in FIG. 1) can map one or more of the topological modules502, 504 onto the graph 506. The MRS can determine that at least aportion of the graph 506 represents one or more topological modules 508,510 by, for example, pattern matching and/or pattern recognition.Accordingly, the topological module 502 has the same network structureas the topological module 508 mapped onto graph 506. Further, thetopological module 504 has the same network structure as the topologicalmodule 510 mapped onto graph 506. When viewed separately, thetopological modules 502, 504 do not necessarily convey any informationabout their association. However, when the topological modules 502, 504are mapped onto the graph 506 as topological graphs 508, 510, the MRScan determine that the topological graphs 508, 510 have overlappingnodes “A” 512 and “B” 514. Further, the topological modules 502, 504viewed separately may not necessarily convey the information that thenodes (A 512, B 514) are both highly connected nodes, because each ofthese nodes 512, 514 in the topological modules 502, 504 have only threeedges connected thereto. However, when mapped onto the graph 506 astopological graphs 508, 510, the MRS can determine that the overlappingnodes (A 512, B 514) are both highly connected nodes (e.g., these nodes512, 514 are hubs) for both topological modules 508, 510. Accordingly,by the mapping process, the MRS can determine that there is an inferencerelationship, represented by the overlapping nodes (A 512, B 514)between the two disease states represented by the topological modules502, 504.

FIGS. 6-8 illustrate an embodiment of the AGI method, which can beperformed by the MRS (100 illustrated in FIG. 1) for simplifying acomplex network of physiological features to few highly connected hubsfor making an inference between two sets of topological modules.

FIG. 6 illustrates a visual (e.g., schematic) representation 600 of atopological module 602 of a disease state (e.g., emergent disease)mapped onto a graph 604 (which has the same topology as the graph 404illustrated in FIG. 4 with respect to nodes and edges connecting thenodes).

FIG. 7 illustrates a visual (e.g., schematic) representation 700 of thetopological module 602, wherein overlapping nodes 702, 704, 706, 708(filled-in circles) of the topological module 602 and anothertopological module (i.e., 402 illustrated in FIG. 4) are shown. Althoughnot shown in FIG. 7, FIG. 4 shows the module 402 having the highlyconnected nodes 408 labeled as A, B, C, and D.

FIG. 8 illustrates a visual (e.g., schematic) representation 800 of aportion of the graph (604 illustrated in FIGS. 6 and 7) showing only theinference nodes 702, 704, 706, 708. These inference nodes 702, 704, 706,708 can be defined to be a macro node 802 for the two topologicalmodules (402 illustrated in FIGS. 4 and 602 illustrated in FIG. 6). Thatis, the macro node 802 is a network of the fewest number of the mosthighly connected disease associated nodes 702, 704, 706, 708 for bothtopological modules (402 illustrated in FIGS. 4 and 602 illustrated inFIG. 6). Accordingly, the AGI method carried out by the MRS candetermine that the same macro node 802 exists for the disease statesrepresented as topological modules (402 illustrated in FIGS. 4 and 602illustrated in FIG. 6). This inference relationship data can be storedinto the memory of the MRS. The macro node 802 and inferencerelationship data can be used in an inference process to determine anemergent disease and pre-emergent disease. For example, if thetopological module 402 (illustrated in FIG. 4) is an emergent disease,and the topological module 602 (illustrated in FIG. 6) is a pre-emergentdisease, the inference nodes determined by the MRS allows the MRS topredict that the emergence of the disease represented by the topologicalmodule 402 (illustrated in FIG. 4) means that the disease represented bythe topological module 602 (illustrated in FIG. 6) will or is highlylikely to emerge. Numerical analysis of the node values can be performedby the MRS to calculate the risks of the emergence of pre-emergentdisease state represented by the topological module 602 (illustrated inFIG. 6).

FIG. 9 illustrates an example of the numerical analysis of node valueswhich can be performed by the MRS. The data/features contained within adisease module are typically nonlinear and vacillate among various stateweights. A disease is rarely a consequence or reflection of a singledata/feature but reflects the perturbations of a complex network thatlinks multiple data/features. Disease module behavior is not random buta series of general organizing principals in their structure andevolution. Data/feature nodes are interdependent and continuouslyreadjust in order to stabilize their integrated state.

FIG. 9 shows a chart 900 for performing a numerical analysis of valuesfor five nodes 902, 904, 906, 908, 910. For example, the nodes 902, 904,906, 908, 910 can be hubs of a topological module for a disease state.The topological module may be mapped onto a graph, wherein each of thenodes 902, 904, 906, 908, 910 has physiological data of an individualfor creating an individualized graph.

A scaled value is assigned by the MRS for the physiological data foreach of the nodes 902, 904, 906, 908, 910. That is, each node 902, 904,906, 908, 910 represents a group of highly connected physiologicalfeatures that represent a disease state, and physiological data has beenreceived by the MRS. The MRS scales the physiological data for each ofthe physiological features (nodes 902, 904, 906, 908, 910) and assignsthe scaled value to the nodes 902, 904, 906, 908, 910. The scaled valuescan be, for example but not limited to, integer values from 0 to 3,wherein 0=normal, 1=mild, 2=moderate, 3=severe. The scaled valuesrepresent a degree of perturbation for each of the physiological feature(node 902, 904, 906, 908, 910).

The chart 900 shows that the physiological data of the individual forthe physiological feature (node 902) is severely perturbed from normal(scaled value=3).

The chart 900 shows that the physiological data of the individual forthe physiological feature (node 904) is moderately perturbed from normal(scaled value=2).

The chart 900 shows that the physiological data of the individual forthe physiological feature (node 906) is mildly perturbed from normal(scaled value=1).

The chart 900 shows that the physiological data of the individual forthe physiological feature (node 908) is mildly perturbed from normal(scaled value=1).

The chart 900 shows that the physiological data of the individual forthe physiological feature (node 910) is severely perturbed from normal(scaled value=3).

The MRS can calculate a Total module weight 912 from the scaled valuesof the five nodes 902, 904, 906, 908, 910. For example, the Total moduleweight 912 can be a sum of the scaled values of the five nodes 902, 904,906, 908, 910, which is 10. A maximum modular weight value can bedetermined based on the number of nodes in the topological module andthe maximum scale value (in this example, 3) for each node. The maximummodular weight is a value represented by the maximum scale value (inthis example, 3) multiplied by the number of nodes (in this example, 5).Thus, in this example, the maximum modular weight is 3×5=15.

The MRS can calculate a Variance 914 of the individual's physiologicaldata from normal by, for example, dividing the Total module weight 912(value of 10) by a maximum modular weight (value of 15). This ratio isthe Variance 914. Accordingly, the Variance 914 can be a real numberscaled from 0.0 to 1.0, wherein 0.0 represents absolute normalcy, and1.0 represents severe variance from normal. Another form of numericalscale may be used. In this example, the Variance 914 is 10/15=0.67. Thatis, the physiological state of this individual has a Variance 914 fromnormal of 0.67. Accordingly, a single numerical value can be used inquantifying numerically and computationally determining perturbationsand variance of an individual's physiological state from normal withrespect to a clinical descriptor (disease state). In this example, theVariance 914 of 0.67 would indicate a “moderate” clinical state 916 inrelation to the disease state represented by the nodes 902, 904, 906,908, 910. An inference state 918 is a value determined by the maximumscaled value multiplied by the weight average value. The inference state918 is related to a probability of an event given the occurrence ofanother event. That is, the inference state 918 value can be used indetermining a quantifiable risk of emergence of another disease state.In this example, the maximum scaled value is 3, and the weight averagevalue is 0.67. Thus, the inference state 916 value is 3×0.67=2.1. Thesteps in the process illustrated in FIG. 9 and described in relation toFIG. 9 are performed by the MRS.

FIG. 10 illustrates another example of the steps carried out by the MRSusing the AGI method.

In Step 1, the MRS performs a node aggregation step to a network ofnodes, reducing the total network (e.g., graph) using node aggregationinto a small singly connected graph 1000. The MRS identifies a macronode, or a set of fewest highly connected nodes 1002, 1004, 1006 (e.g.,hubs) in the graph 1000. The MRS can computationally represent thesehubs 1002, 1004, 1006 as an aggregated set of features (A, B, C) 1008forming the macro node 1010.

In Step 2, the MRS transforms the graph 1000 into a scaled value (e.g.numerical state) 1012 for each hub 1002, 1004, 1006, wherein each of thescaled value represents a clinically equivalent range for thephysiological feature represented by the hub 1002, 1004, 1006. Forexample, the scaled values can represent qualitative states 1014 ofnormal, mild, moderate, and severe.

In the example shown, the physiological features represented by nodes A1002 and C 1006 are determined by the MRS to be “moderate” and to havethe scaled values of 2. The physiological feature represented by node B1004 is determined by the MRS to be “severe” and to have the scaledvalue of 3.

In Step 3, the MRS determines that the number of hubs 1002, 1004, 1006defining the disease state (topological module) is 3 1016, the summedgroup state is 7 (e.g., 2+2+3) 1018, the maximum state of the diseasestate is 9 (e.g., physiological condition of an emergent disease state)1020, and the current condition value in relation to the disease stateis 0.78 (e.g., summed group state divided by the maximum state=7/9=0.78)1022. This current condition value infers the magnitude of variance fromnormal in relation to the disease state represented by the topologicalmodule defined by the hubs 1002, 1004, 1006.

Following are examples of hubs of a macro node, which defines a diseasestate, and clinical ranges of the physiological data for the qualitativestates of normal, mild, moderate, and severe. The qualitative states ofnormal, mild, moderate, and severe can be assigned scaled values of 0,1, 2, and 3, respectively. The following multi-domain examples and manyother topological modules can be stored in the memory of the MRS. TheMRS can receive patient data having physiological data for the followingfeatures, apply the AGI method described herein, and determine and/orpredict the related emergent disease state and/or pre-emergent diseasestate. The MRS can receive patient data having physiological data forthe following features, apply the AGI method described herein, anddetermine a risks for the related pre-emergent disease states frombecoming emergent.

Example 1: Endocrinology Disease State: Diabetes

Hub Normal Mild Moderate Severe Hgb A1c % <5.7 to 6.4 ≥6.4 to 7.5 >7.5to 8.5  >8.5 GFR Glomerular ≥70   70 to 50 50 to 30 <30 filtration ratemL/min/1.73 m² Hemoglobin 13.8 to 17.2 M <12 12 to 10 <10 gm/dL 12.1 to15.1 F Weight (BMI) <25.0 25.0 29.9 30-39.9 ≥40

Example 2: Gastroenterology Disease State: Celiac Disease

Hub Normal Mild Moderate Severe TTG antibody Test Negative VariablePositive (anti-transglutaminase 10X abnormal antibodies) EMA-IgA Anti-Negative Variable Positive endomysial antibody HLA Genetic TissueNegative Variable Positive (30% Type (human leukocyte over diagnosis)antigen Small Bowel Biopsy Negative Sampling Error Positive Variable

Example 3: Hepatology Disease State: Light Chain Amyloidosis

Hub NORMAL or ABSENT PRESENT Serum-Immune Fixation Yes/No Yes/NoUrine-Immune Fixation Yes/No Yes/No Free Immunoglobulin Light Chain >⅓ratio >3 ratio Kappa/Lambda ratio

Example 4: Oncology Disease State: Pancreatic Cancer

Hub Normal Mild Moderate Severe CA 19-9 (carbohydrate 37-50 >100 antigen19-9) Cancer antigen 19-9 Chronic Epigastric None Mild Chronic Painintermittent Severe Jaundice None Document Screening CT Image of an NonePositive Positive epigastric mass (Possible) (Definite)

Example 5: Pulmonology Disease State: Obstructive Airway

Hub Normal Mild Moderate Severe FEV1/FVC ratio <70% >70% Total LungCapacity >80% 60-80 50-50 <50% Residual Volume ≤100 150 (RV/TLC ratio)DLC Diffusing Lung >80% <30% Capacity

Example 6: Rheumatology Disease State: Rheumatoid Arthritis

Hub Normal Low Moderate High Tender Joints 0 — — 28 Swollen Joints 0 — —28 Sedimentation 0-15 0-20 men — — — Rate (mm/hr) 0-20 0-30 femC-Reactive Protein- <1.0 <3.0 — — hs mg/L Pain Scale (%) 0 — — 100Global Patient 0 — — 100 Scale (%) Global Physician 0 — — 100 Scale (%)

FIG. 11 illustrates how the MRS determines and differentiates between anemergent disease state vs. non-emergent (or pre-emergent) disease state.In Status A 1100 and Status B 1102, the hubs (e′=myocardial relaxation;E/e′=filling pressure; LAVI=chronicity of filling pressure; E/A andDT=acuity of filling pressure) for heart failure state are provided. TheMRS reads the hubs 1104 from the memory of the MRS and processesphysiological data received by the MRS, and determines the quantitativeand qualitative state for each of the hubs 1104. In State A 1100, thequalitative states for the hubs 1104 are e′=moderate, E/e′=mild,LAVI=moderate, E/A=moderate, and DT=moderate. The MRS can perform theAGI method of quantitative determination of variance and conclude thatthe received physiological data in Status A 1100 is asymptomatic. Thatis, Status A 1100 is determined to have non-emergent disease for heartfailure.

In contrast, for Status B 1102, the MRS reads the hubs 1104 from thememory of the MRS and processes a different (either of anotherindividual from Status A, or from the same individual as Status A but ata different time) physiological data received by the MRS, and determinesthe quantitative and qualitative state for each of the hubs 1104. InState B 1102, the qualitative states for the hubs 1104 are e′=moderate,E/e′=severe, LAVI=severe, E/A=severe, and DT=severe. The MRS can performthe AGI method of quantitative determination of variance and concludethat the received physiological data in Status B 1102 has an emergentdisease, e.g., heart failure.

FIG. 12 illustrates an example of an individual's physiological data1200 collected along a time dimension (e.g., data has been collectedmany times at different times). The hubs 1202 (Left atrial volume indexLAVI, Tissue Doppler myocardial relaxation e′, Left ventricular fillingpressure E/e′, Mitral valve deceleration time DT, Mitral valve MV)define the disease state of Atrial Fibrillation. At one point in time,the MRS has determined, based on the analysis of the values for the hubs1202, that the individual does not have an emergent Atrial Fibrillationdisease state. For example, the qualitative states of the hubs 1202 fortime 1204 are all normal.

Yet, at another point in time 1206, the MRS has determined, based on theanalysis of the values for the hubs 1202, that the individual does havean emergent Atrial Fibrillation disease state. For example, thequalitative states of the hubs 1202 for time 1206 are LAVI is abnormallylarge, e′ is abnormally low, E/e′ is highly variable, MV and DT are bothabnormally variable. The MRS can determine during the time period 1208that the individual is heading towards the emergent condition shown attime 1206. Periodic measurements of the physiological data for the hubs1202 during the time 1206 received by the MRS can be used in determiningthe total variance value (or disease intensity). As described above (forexample, FIG. 9), the disease intensity is a single numerical value (pertaking of a physiological data), which can be used in quantifyingnumerically and computationally determining perturbations and varianceof an individual's physiological state from normal with respect to adisease state. This disease intensity (e.g., weighted degree ofvariance) value can be measured at different times, and the MRS candetermine a trend in the change of the disease intensity value.

For example, FIG. 13 shows a chart 1300 showing a change in the diseaseintensity 1302 (Y-axis) along a time dimension 1304 (X-axis). The MRScan determine the intensity state 1302 of two disease modules (e.g.,chronicity 1306 and acuity 1308) over the time dimension 1304. The MRScalculates and stores disease intensity values of the disease modules1306, 1308. The MRS can infer a relationship between the two diseasemodules 1306, 1308 because the change in the data for the two diseasemodules along the time dimension shows an interdependence phenomenon.Chronicity (historical burden of left ventricular filling pressure) andacuity (burden of filling pressure at the time of data acquisition) canbe drawn as a chart 1302 along the time dimension 1304 (the MRS cancomputationally determine this relationship without necessarily graphingthe chart 1304). The acuity 1308 evolves to a lower degree of intensityand chronicity to a higher degree of intensity. Based on the timedependent changes the MRS can provide an output via the I/O device(s)for a user so that the user can appreciate the interdependency of dataand make a more informed management decision.

FIG. 14 illustrates an example of a chart 1400 resulting from the MRSperforming a metadata analysis of temporal data, for the disease HeartFailure, using hubs e′, LAVI, E/e′, DT, and E/A. The MRS determines adisease intensity (or variance) from a numerical analysis using the AGImethod for an individual's physiological data in relation to a diseasemodule. The chart 1400 includes a disease intensity 1402 (Y-axis) havinga value of 0 (normal) to 1 (severe). The chart 1400 has the timedimension along the X-axis. Thus, the MRS determines a first diseaseintensity 1404 for a 1st exam date. The MRS determines a second diseaseintensity 1406 for a 2nd exam date. The MRS determines a third diseaseintensity 1408 for a 3rd exam date. The MRS determines a fourth diseaseintensity 1410 for a 4th exam date. A table of the disease intensitiesis shown in the table below. Further, based on the differences betweenthe disease intensities, the MRS can determine qualitative informationas to whether the individual is improving or deteriorating over time.

1st exam 2nd exam 3rd exam 4th exam date date date date DiseaseIntensity 0.89 0.61 0.5 0.79 Change Beginning Improve ImproveDeteriorateAccordingly, the MRS metadata analysis is capable of exhibiting a robustexpression of disease status and tracking of management decisions.

The MRS can predict (e.g., cause-and-effect), based on the quantifiednumerical analysis (e.g., weighted averaging) using the AGI method ofprocessing hubs related to a topological module and emergent conditions,one or more events that are pre-emergent. Further, based on thepredicted events, the MRS can provide an output via I/O device(s) of asuggested physiological and/or medical management/treatment forpreventing and/or altering and/or delaying the emergence of thepredicted event.

The table below provides some examples of the MRS making a determinationof an emergent condition, predicted event(s), and suggested managementoutput (e.g., disease management based on surrogate cause-and-effectmodeling).

Emergent Predicted Suggested Management Hubs (features) = Condition =Event(s) = Output Blood Pressure + = Hypertensive = Stroke, Heart =Medical Management Cardiac Dysfunction Heart Disease Failure, RenalFailure, etc. Ejection Fraction + = Systolic Cardiac = Heart Failure, =Medical/Surgical Systolic myocyte Dysfunction Coronary Artery Managementexcursion (fibrosis) + Disease wall motion Myocyte relaxation + =Diastolic Cardiac = Stroke, Congestive = Medical Management elevation LVfilling Dysfunction Heart Failure pressure Multivariable echo/Doppler =Valve Disease = Stroke, Heart = Medical/Surgical hemodynamic profileFailure, Death Management Myocardial relaxation (e′) + = AtrialFibrillation = Stroke, Heart = Medical/Surgical chronicity of LV fillingFailure, Death Management pressure

The MRS can proceed with assisted and/or autonomous learning based onthe graph, topological modules, and physiological data received andstored in the memory of the MRS. For example, where the MRS performsvariance determinations based on hubs of a particular topologicalmodule, the MRS can include one or more nodes (having fewer connectionsthan the hubs) of the topological node in addition to the hubs. Further,the MRS can substitute one or more of the hubs with one or more of thenodes. If the resulting variance and other quantitative analysis valuesare the same or substantially similar to the values determined by usingonly the hubs, the MRS stores that data, so that in certain situationswhen a set of physiological data is received by the MRS and the receiveddata is lacking towards a value related to one or more of the hubs, theMRS “knows” that another data value (if available) can be substitutedand/or used in place of the missing hub value in making thedetermination and/or prediction. The MRS can modify learning based onuser and/or processing behavior.

With regard to the foregoing description, it is to be understood thatchanges may be made in detail without departing from the scope of thepresent invention. It is intended that the specification and depictedembodiment to be considered exemplary only, with a true scope and spiritof the invention being indicated by the broad meaning of the claims.

What is claimed is:
 1. A computer-implemented method for determining adisease state of a patient, the method comprising: a processor accessingfrom a computer-readable non-transitory memory, computer-readable datarepresenting a general graph having nodes of physiological conditions,wherein each of the physiological conditions is represented by each ofthe nodes, wherein each of the nodes is connected by one or more edgesrepresenting correlations between the nodes; the processor accessingfrom the computer-readable non-transitory memory, computer-readable dataof physiological conditions of a patient; the processor executingcomputer-readable instructions for quantification of the physiologicalconditions of the patient; the processor transforming the physiologicalconditions of the patient to node data by performing quantification ofthe physiological conditions of the patient; the processor executingcomputer-readable instructions for associating the node data to thegeneral graph; the processor creating an individualized graph byassociating the node data to the general graph; the processor storingthe individualized graph to the computer-readable non-transitory memory;the processor accessing from the computer-readable non-transitorymemory, computer-readable data representing a topological module ofnodes connected by edges, wherein the topological module represents thedisease state; and the processor executing computer-readableinstructions for mapping the topological module representing the diseasestate to the individualized graph, wherein the disease state of thepatient is determined.
 2. The computer-implemented method as in claim 1,further comprising: the processor mapping the topological module to theindividualized graph, wherein the disease state is an emergent diseasestate.
 3. The computer-implemented method as in claim 1, furthercomprising: the processor mapping the topological module to theindividualized graph, wherein the disease state is a pre-emergentdisease state.
 4. A computer-implemented method for creating a graph fordetermining a disease state, the method comprising: storing on acomputer-readable non-transitory memory, computer-readable datarepresenting a general graph of physiological conditions, wherein eachof the physiological conditions is represented as a node of the generalgraph, wherein each node is connected by one or more edges representingcorrelations between the nodes; and storing on the computer-readablenon-transitory memory, computer-readable data representing a topologicalmodule of nodes connected by edges, wherein the topological modulerepresents the disease state, and the topological module can be mappedonto a portion of the general graph.
 5. A computer-implemented methodfor determining a pre-emergent disease state of a patient, the methodcomprising: a processor accessing from a computer-readablenon-transitory memory, computer-readable data representing physiologicalconditions represented as nodes, wherein at least one of the nodes has acorrelation to another one of the nodes; the processor accessing fromthe computer-readable non-transitory memory, computer-readable data offirst physiological conditions of a patient collected at a first timeperiod; the processor transforming the first physiological conditions ofthe patient to first node data by performing quantification of the firstphysiological conditions of the patient; the processor accessing fromthe computer-readable non-transitory memory, computer-readable data ofsecond physiological conditions of the patient collected at a secondtime period; the processor transforming the second physiologicalconditions of the patient to second node data by performingquantification of the second physiological conditions of the patient;the processor accessing from the computer-readable non-transitorymemory, computer-readable data representing a topological module ofnodes, wherein the topological module represents the pre-emergentdisease state; the processor mapping the topological module to the firstnode data; the processor mapping the topological module to the secondnode data; and the processor determining the pre-emergent disease stateof the patient based on the mapping the topological module to the firstnode data and to the second node data.
 6. A medical general intelligencecomputer, comprising: a processor; and a computer-readablenon-transitory memory in communication with the processor, thecomputer-readable non-transitory memory including processor-executableinstructions for determining a pre-emergent disease state of a patient,wherein when the processor executes the processor-executableinstructions, the processor accesses from the computer-readablenon-transitory memory, computer-readable data representing physiologicalconditions represented as nodes, wherein at least one of the nodes has acorrelation to another one of the nodes; the processor accesses from thecomputer-readable non-transitory memory, computer-readable data of firstphysiological conditions of a patient collected at a first time period;the processor transforms the first physiological conditions of thepatient to first node data by performing quantification of the firstphysiological conditions of the patient; the processor accesses from thecomputer-readable non-transitory memory, computer-readable data ofsecond physiological conditions of the patient collected at a secondtime period; the processor transforms the second physiologicalconditions of the patient to second node data by performingquantification of the second physiological conditions of the patient;the processor assesses from the computer-readable non-transitory memory,computer-readable data representing a topological module of nodes,wherein the topological module represents the pre-emergent diseasestate; the processor maps the topological module to the first node data;the processor maps the topological module to the second node data; andthe processor determines the pre-emergent disease state of the patientbased on the mapping the topological module to the first node data andto the second node data.